지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2011.10
- 수록면
- 684 - 687 (4page)
이용수
초록· 키워드
오류제보하기
Mobile robot localization is the task of estimating the robot pose in a given environment. Among many localization techniques, Monte Carlo localization (MCL) is known to be one of the most reliable methods for pose estimation of a mobile robot. However, as outdoor environments are large and contain many complex objects, it is difficult to robustly estimate the robot pose using MCL in outdoor environments. Therefore, this study proposes a novel approach, the Hausdorff distance-based matching method using the objects commonly observed from air and ground (COAG) features for outdoor MCL algorithm. The Hausdorff distance is exploited to measure the similarity between the COAG features extracted from the robot and the elevation map. The experimental results in real environments show that the success rate of outdoor MCL increases and the proposed method is useful for robust outdoor localization using an elevation map.
목차
Abstract
1. INTRODUCTION
2. MONTE CARLO LOCALIZATION (MCL)
3. COAG FEATURES
4. HAUSDORFF DISTANCE MATCHING
5. MCL-BASED LOCALIZATION USING HAUSDORFF DISTANCE
6. CONCLUSION
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2014-569-000913347